Modelling hyperexcitability in human cerebral cortical organoids: Oxygen/glucose deprivation most effective stimulant
Bibliographic record
Abstract
Epilepsy is a common neurological disorder that affects 1% of the global population. The neonatal period constitutes the highest incidence of seizures. Despite the continual developments in seizure modelling and anti-epileptic drug development, the mechanisms involved in neonatal seizures remain poorly understood. This leaves infants with neonatal seizures at a high risk of death, poor prognosis of recovery and risk of developing neurological disorders later in life. Current in vitro platforms for modelling adult and neonatal epilepsies – namely acute cerebral brain slices or cell-derived cultures, both derived from animals-either lack a complex cytoarchitecture, high-throughput capabilities or physiological similarities to the neonatal human brain. Cerebral organoids, derived from human embryonic stem cells (hESCs), are an emerging technology that could better model neurodevelopmental disorders in the developing human brain. Herein, we study induced hyperexcitability in human cerebral cortical organoids – setting the groundwork for neonatal seizure modelling - using electrophysiological techniques and pharmacological manipulations. In neonatal seizures, energy failure - specifically due to deprivation of oxygen and glucose - is a consistent and reliable seizure induction method that has been used to study the underlying cellular and molecular mechanisms. Here, we applied oxygen-glucose deprivation (OGD) as well as common chemoconvulsants in 3-7-month-old cerebral organoids. Remarkably, OGD resulted in hyperexcitability, with increased power and spontaneous events compared to other common convulsants tested at the population level. These findings characterize OGD as the stimulus most capable of inducing hyperexcitable changes in cerebral organoid tissue, which could be extended to future modelling of neonatal epilepsies in cerebral organoids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".